Single molecule tracking and analysis framework including theory-predicted parameter settings
Abstract Imaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require ad...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-88802-7 |
| _version_ | 1818857824215629824 |
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| author | Timo Kuhn Johannes Hettich Rubina Davtyan J. Christof M. Gebhardt |
| author_facet | Timo Kuhn Johannes Hettich Rubina Davtyan J. Christof M. Gebhardt |
| author_sort | Timo Kuhn |
| collection | DOAJ |
| description | Abstract Imaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions. |
| first_indexed | 2024-12-19T08:46:32Z |
| format | Article |
| id | doaj.art-e322431a14bd43cda465d8be9e63666c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-19T08:46:32Z |
| publishDate | 2021-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e322431a14bd43cda465d8be9e63666c2022-12-21T20:28:50ZengNature PortfolioScientific Reports2045-23222021-05-0111111210.1038/s41598-021-88802-7Single molecule tracking and analysis framework including theory-predicted parameter settingsTimo Kuhn0Johannes Hettich1Rubina Davtyan2J. Christof M. Gebhardt3Institute of Biophysics, Ulm UniversityInstitute of Biophysics, Ulm UniversityInstitute of Biophysics, Ulm UniversityInstitute of Biophysics, Ulm UniversityAbstract Imaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.https://doi.org/10.1038/s41598-021-88802-7 |
| spellingShingle | Timo Kuhn Johannes Hettich Rubina Davtyan J. Christof M. Gebhardt Single molecule tracking and analysis framework including theory-predicted parameter settings Scientific Reports |
| title | Single molecule tracking and analysis framework including theory-predicted parameter settings |
| title_full | Single molecule tracking and analysis framework including theory-predicted parameter settings |
| title_fullStr | Single molecule tracking and analysis framework including theory-predicted parameter settings |
| title_full_unstemmed | Single molecule tracking and analysis framework including theory-predicted parameter settings |
| title_short | Single molecule tracking and analysis framework including theory-predicted parameter settings |
| title_sort | single molecule tracking and analysis framework including theory predicted parameter settings |
| url | https://doi.org/10.1038/s41598-021-88802-7 |
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